From my model, I'm asked to determine which variables are statistically significant.

```
fitted.model <- lm(spending ~ sex + status + income, data=spending)
```

My results were as follows:

```
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.55565 17.19680 1.312 0.1968
sex **-22.11833** 8.21111 -2.694 0.0101 *
status 0.05223 0.28111 0.186 0.8535
income 4.96198 1.02539 4.839 1.79e-05 ***
verbal -2.95949 2.17215 -1.362 0.1803
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 22.69 on 42 degrees of freedom
Multiple R-squared: 0.5267, Adjusted R-squared: 0.4816
F-statistic: 11.69 on 4 and 42 DF, p-value: 1.815e-06.
```

**Question:** Do I have to look at the last column? If so, then `sex`

and `income`

would be statistically significant.

## Best Answer

Yes, based on the output,

`sex`

and`income`

are statistically significant.`sex`

and possibly`status`

are nominal variables, so it's odd that they appear in the model as is. It could work, if they are 0/1 variables, but it still opens up the potential for error.To be on the safe side, for

`sex`

and any other nominal variable, include it in the model like this:`factor(sex)`

: